@@ -32,7 +32,7 @@ def uses_nth_neighbors(n):

     The next function is a part of the `curvature_loss_function` function.

     >>> @uses_nth_neighbors(1)

-    ...def triangle_loss(xs, ys):

+    ... def triangle_loss(xs, ys):

     ...    xs = [x for x in xs if x is not None]

     ...    ys = [y for y in ys if y is not None]

     ...

@@ -92,7 +92,7 @@ lines. However, as always, when you sample more points the graph will

 become gradually smoother.

 Using any convex shape as domain

+................................

 Suppose you do not simply want to sample your function on a square (in 2D) or in

 a cube (in 3D). The LearnerND supports using a `scipy.spatial.ConvexHull` as

@@ -46,11 +46,14 @@ tl;dr, one can use the following *loss functions* that

 + `adaptive.learner.learner1D.default_loss`

 + `adaptive.learner.learner1D.uniform_loss`

++ `adaptive.learner.learner1D.curvature_loss_function`

 + `adaptive.learner.learner2D.default_loss`

 + `adaptive.learner.learner2D.uniform_loss`

 + `adaptive.learner.learner2D.minimize_triangle_surface_loss`

 + `adaptive.learner.learner2D.resolution_loss_function`

+Whenever a loss function has `_function` appended to its name, it is a factory function

+that returns the loss function with certain settings.

 Uniform sampling

 ~~~~~~~~~~~~~~~~